Object detection plays an important role in many video applications, such as computer vision, and video surveillance systems. In general, object detection is one of the major factors for the success of video systems.
Japan Patent No. 61003591 disclosed a technique for storing background picture in the first picture memory, and store image containing objects in the second picture memory. By subtracting the data in these two picture memories, the result is the scene change, where the objects are.
U.S. patent and publication documents also disclosed several techniques for object detection. For example, U.S. Pat. No. 5,099,322 uses an object detector to detect abrupt changes between two consecutive images, and uses a decision processor to determine whether scene changes occur by means of feature computing. U.S. Pat. No. 6,999,604 uses a color normalizer to normalize the colors in an image, and uses a color transformer for color transformation so that the image can be enhanced and the area suspected of an object is enhanced to facilitate object detection. Finally, a comparison against the default color histogram is performed, and a fuzzy adaptive algorithm is used to find the moving object in the image.
U.S. Patent Publication No. 2004/0017938 disclosed a technique with a default color feature of objects. During detection, anything that matches the default color feature is determined to be an object. U.S. Patent Publication No. 2005/0111696 disclosed a technique with long exposure to capture the current image at a low illumination, and comparing the current image against the previous reference image to detect the changes. U.S. Patent Publication No. 2004/0086152 divides the image into blocks, and compares the current image block against the previous corresponding image block for the difference of frequency domain transformation parameter. When the difference exceeds a certain threshold, the image block is determined to have changed.
Gaussian Mixture Model (GMM) is usually used for modeling each pixel or region to make the background model adaptive to the changing illumination. Those pixels that do not fit the model are considered as foreground.
Dedeoglu Y. disclosed an article in 2005, “Human Action Recognition Using Gaussian Mixture Model Based Background Segmentation,” using Gaussian Mixture Model to perform real-time moving object detection.
Hidden Markov Model (HMM) is used for modeling a non-stationary process, and uses the time-axis continuity constraint in the continuous pixel intensity. In other words, if a pixel is detected as foreground, the pixel is expected to stay as foreground for a period of time. The advantages of HMM are as follows. (1) Selection of training data is not required, and (2) Using different hidden states to learn the statistical characteristics of foreground and background from a mixed sequence of foreground symbols and background symbols.
An HMM can be expressed as H:=(N,M,A,π,P1,P2), where N is the number of states, M is the number of symbols, A is the state transition probability matrix, A={aij,i,j=1, . . . N}, aij is the transiting probability from state i to state j, π={π1, . . . , πN}, πi is the initial probability of state i, and P=(pi, . . . , pn), pi is the probability of state i.
J. Kato presented a technique in the article, “An HMM-Based Segmentation Method for Traffic Monitoring Movies,” IEEE Trans. PAMI, Vol. 24, No. 9, pp. 1291-1296, 2002, using a grey scale to construct an HMM on the time axis for each pixel. There are three states for each pixel, i.e. background state, foreground state, and shadow state, for detecting objects.
FIG. 1 shows a schematic view of a flowchart of a conventional HMM. As shown in FIG. 1, a conventional HMM procedure includes three steps: (1) initializing HMM parameters, as shown in step 101; (2) training stage, that is, estimating and updating the HMM parameters through Baum-Welch algorithm, as shown in step 103; and (3) using Viterbi algorithm and the HMM parameters from the previous step to estimate the state for input data (foreground state and background state), as shown in step 105. Baum-Welch algorithm is used for training HMM parameters.
Using Baum-Welch algorithm, the state transition probability matrix A, the initial probability πi of each state i, and the probability pi of each state i can be trained from the previous sample and updated. The Baum-Welch algorithm is an iterative likelihood maximization method. Therefore, it is time-consuming for estimating and updating the HMM parameters.